704 citations · 4.1k across the 112 of their papers we have counts for
25 papers · 1 filter
Hausdorff Point Convolution with Geometric Priors
Pengdi Huang, Liqiang Lin, Fuyou Xue +3
Without a shape-aware response, it is hard to characterize the 3D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of Hausdor…
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
Or Patashnik, Dov Danon, Hao Zhang +1
State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupe…
Neural Alignment for Face De-pixelization
Maayan Shuvi, Noa Fish, Kfir Aberman +2
We present a simple method to reconstruct a high-resolution video from a face-video, where the identity of a person is obscured by pixelization. This concealment method is popular…
Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects
Jiahui Lyu, Bojian Wu, Dani Lischinski +2
Capturing the 3D geometry of transparent objects is a challenging task, ill-suited for general-purpose scanning and reconstruction techniques, since these cannot handle specular li…
SketchPatch: Sketch Stylization via Seamless Patch-level Synthesis
Noa Fish, Lilach Perry, Amit Bermano +1
The paradigm of image-to-image translation is leveraged for the benefit of sketch stylization via transfer of geometric textural details. Lacking the necessary volumes of data for…
Object Properties Inferring from and Transfer for Human Interaction Motions
Qian Zheng, Weikai Wu, Hanting Pan +3
Humans regularly interact with their surrounding objects. Such interactions often result in strongly correlated motion between humans and the interacting objects. We thus ask: "Is…
Self-Sampling for Neural Point Cloud Consolidation
Gal Metzer, Rana Hanocka, Raja Giryes +1
We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. Unlike other point upsampling methods which analyze shapes via loc…
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Elad Richardson, Yuval Alaluf, Or Patashnik +4
We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style…
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
Rinon Gal, Amit Bermano, Hao Zhang +1
We present MRGAN, a multi-rooted adversarial network which generates part-disentangled 3D point-cloud shapes without part-based shape supervision. The network fuses multiple branch…
Deep Geometric Texture Synthesis
Amir Hertz, Rana Hanocka, Raja Giryes +1
Recently, deep generative adversarial networks for image generation have advanced rapidly; yet, only a small amount of research has focused on generative models for irregular struc…
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
Mingyi Shi, Kfir Aberman, Andreas Aristidou +4
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video.While previous methods rely on either rigging or inve…
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Jinming Cao, Yangyan Li, Mingchao Sun +5
Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise c…
Towards a Neural Graphics Pipeline for Controllable Image Generation
Xuelin Chen, Daniel Cohen-Or, Baoquan Chen +1
In this paper, we leverage advances in neural networks towards forming a neural rendering for controllable image generation, and thereby bypassing the need for detailed modeling in…
Point2Mesh: A Self-Prior for Deformable Meshes
Rana Hanocka, Gal Metzer, Raja Giryes +1
In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud. Instead of explicitly specifying a prior that encodes the expected…
Unpaired Motion Style Transfer from Video to Animation
Kfir Aberman, Yijia Weng, Dani Lischinski +2
Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most e…
Skeleton-Aware Networks for Deep Motion Retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski +3
We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Imp…
Face Identity Disentanglement via Latent Space Mapping
Yotam Nitzan, Amit Bermano, Yangyan Li +1
Learning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to cont…
Image Morphing with Perceptual Constraints and STN Alignment
Noa Fish, Richard Zhang, Lilach Perry +3
In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances. Intermediates must remain faithfu…
Single Pair Cross-Modality Super Resolution
Guy Shacht, Sharon Fogel, Dov Danon +2
Non-visual imaging sensors are widely used in the industry for different purposes. Those sensors are more expensive than visual (RGB) sensors, and usually produce images with lower…
Structural-analogy from a Single Image Pair
Sagie Benaim, Ron Mokady, Amit Bermano +2
The task of unsupervised image-to-image translation has seen substantial advancements in recent years through the use of deep neural networks. Typically, the proposed solutions lea…
PointGMM: a Neural GMM Network for Point Clouds
Amir Hertz, Rana Hanocka, Raja Giryes +1
Point clouds are a popular representation for 3D shapes. However, they encode a particular sampling without accounting for shape priors or non-local information. We advocate for th…
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
Moab Arar, Yiftach Ginger, Dov Danon +3
Many applications, such as autonomous driving, heavily rely on multi-modal data where spatial alignment between the modalities is required. Most multi-modal registration methods st…
A Rotation-Invariant Framework for Deep Point Cloud Analysis
Xianzhi Li, Ruihui Li, Guangyong Chen +3
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to ar…
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation
Wallace Lira, Johannes Merz, Daniel Ritchie +2
We introduce GANHopper, an unsupervised image-to-image translation network that transforms images gradually between two domains, through multiple hops. Instead of executing transla…